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Han Y, Ou Y, Sun H, Kopaczek J, Leonel GJ, Guo X, Brugman BL, Leinenweber K, Xu H, Wang M, Tongay S, Navrotsky A. Thermodynamic properties and enhancement of diamagnetism in nitrogen doped lutetium hydride synthesized at high pressure. Proc Natl Acad Sci U S A 2024; 121:e2321540121. [PMID: 38483993 PMCID: PMC10962990 DOI: 10.1073/pnas.2321540121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 02/12/2024] [Indexed: 03/27/2024] Open
Abstract
Nitrogen doped lutetium hydride has drawn global attention in the pursuit of room-temperature superconductivity near ambient pressure and temperature. However, variable synthesis techniques and uncertainty surrounding nitrogen concentration have contributed to extensive debate within the scientific community about this material and its properties. We used a solid-state approach to synthesize nitrogen doped lutetium hydride at high pressure and temperature (HPT) and analyzed the residual starting materials to determine its nitrogen content. High temperature oxide melt solution calorimetry determined the formation enthalpy of LuH1.96N0.02 (LHN) from LuH2 and LuN to be -28.4 ± 11.4 kJ/mol. Magnetic measurements indicated diamagnetism which increased with nitrogen content. Ambient pressure conductivity measurements observed metallic behavior from 5 to 350 K, and the constant and parabolic magnetoresistance changed with increasing temperature. High pressure conductivity measurements revealed that LHN does not exhibit superconductivity up to 26.6 GPa. We compressed LHN in a diamond anvil cell to 13.7 GPa and measured the Raman signal at each step, with no evidence of any phase transition. Despite the absence of superconductivity, a color change from blue to purple to red was observed with increasing pressure. Thus, our findings confirm the thermodynamic stability of LHN, do not support superconductivity, and provide insights into the origins of its diamagnetism.
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Affiliation(s)
- Yifeng Han
- Center for Materials of the Universe, School of Molecular Sciences, Arizona State University, Tempe, AZ85287
| | - Yunbo Ou
- School for Engineering of Matter Transport and Energy, Arizona State University, Tempe, AZ85287
| | - Hualei Sun
- School of Science, Sun Yat-Sen University, Shenzhen518107, R.P. China
| | - Jan Kopaczek
- School for Engineering of Matter Transport and Energy, Arizona State University, Tempe, AZ85287
- Department of Semiconductor Materials Engineering, Faculty of Fundamental Problems of Technology, Wroclaw University of Science and Technology, Wrocław50-370, Poland
| | - Gerson J. Leonel
- Center for Materials of the Universe, School of Molecular Sciences, Arizona State University, Tempe, AZ85287
| | - Xin Guo
- Eyring Materials Center, Arizona State University, Tempe, AZ85287
| | - Benjamin L. Brugman
- Center for Materials of the Universe, School of Molecular Sciences, Arizona State University, Tempe, AZ85287
| | - Kurt Leinenweber
- Center for Materials of the Universe, School of Molecular Sciences, Arizona State University, Tempe, AZ85287
| | - Hongwu Xu
- Center for Materials of the Universe, School of Molecular Sciences, Arizona State University, Tempe, AZ85287
| | - Meng Wang
- Center for Neutron Science and Technology, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat-Sen University, Guangzhou510275, R.P. China
| | - Sefaattin Tongay
- School for Engineering of Matter Transport and Energy, Arizona State University, Tempe, AZ85287
| | - Alexandra Navrotsky
- Center for Materials of the Universe, School of Molecular Sciences, Arizona State University, Tempe, AZ85287
- School for Engineering of Matter Transport and Energy, Arizona State University, Tempe, AZ85287
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Chen J, Zhang H, Wahl CB, Liu W, Mirkin CA, Dravid VP, Apley DW, Chen W. Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning. Proc Natl Acad Sci U S A 2023; 120:e2309240120. [PMID: 37943836 PMCID: PMC10655557 DOI: 10.1073/pnas.2309240120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/29/2023] [Indexed: 11/12/2023] Open
Abstract
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.
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Affiliation(s)
- Jie Chen
- Department of Mechanical Engineering, Northwestern University, Evanston, IL60208
| | - Hengrui Zhang
- Department of Mechanical Engineering, Northwestern University, Evanston, IL60208
| | - Carolin B. Wahl
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL60208
- International Institute for Nanotechnology, Northwestern University, Evanston, IL60208
| | - Wei Liu
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL60208
| | - Chad A. Mirkin
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL60208
- International Institute for Nanotechnology, Northwestern University, Evanston, IL60208
- Department of Chemistry, Northwestern University, Evanston, IL60208
| | - Vinayak P. Dravid
- Department of Materials Science and Engineering, Northwestern University, Evanston, IL60208
- International Institute for Nanotechnology, Northwestern University, Evanston, IL60208
| | - Daniel W. Apley
- Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL60208
| | - Wei Chen
- Department of Mechanical Engineering, Northwestern University, Evanston, IL60208
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